The Role of Machine Learning in Biofertilizer Industry: From Data Analytics to Predictive Modelling

Agricultural production is an essential aspect of a country’s economic development. Achieving sustainable crop production is consistently a challenge for agriculturists. Farmers have long faced challenges in generating maximum agricultural output due to the constantly shifting weather conditions. The incorporation of machine learning (ML) methods in the biofertilizer sector is a promising advancement in contemporary agriculture. Machine learning algorithms transform input data into novel solutions and diverse methods. Machine learning involves using gained information and rules to predict and make judgments about more data. This research examines the diverse use of machine learning in optimizing the utilization of biofertilizers, including data analytics and predictive modelling. Through the utilization of sophisticated algorithms and insights derived from data, machine learning can profoundly transform agricultural practices, leading to improved crop productivity and a greater focus on environmental responsibility. This chapter explores the important role of machine learning (ML) in determining the future of the biofertilizer sector, based on the current research and developing trends.